In mining applications generally the situational awareness of the operators of large excavation machinery, such as draglines, shovels and excavators, is very important. Current best practice for obstacle avoidance is centred on the training of the operators. Operators primarily rely on the visual sighting of obstacles, and their knowledge of a machine's behaviour to plan safe and effective paths for the machine's operation.
However, human vision is affected in times of limited visibility, for example, at night or during periods of high atmospheric dust content. This has implications for detecting and avoiding obstacles such as large boulders, trucks and other equipment, as well as collision detection with the dig-face, the machine itself, other machines and ground personnel. In addition, there can be large variations in the skill level and productivity of different operators, or of a single operator during a shift cycle.
Various attempts have been made to improve situational awareness for an operator by inclusion of cameras and other means of imaging the scene. Unfortunately, these often distract the operator from their primary task, and still suffer many of the ‘blinding’ limitations caused by dust and low-light.